Research

Preprints

• Optimal Model Selection for Conformalized Robust Optimization
  Yajie Bao, Yang Hu, Haojie Ren, Peng Zhao and Changliang Zou.
  arxiv, 2025+.

Journal publication

• CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control
  Yajie Bao, Yuyang Huo, Haojie Ren and Changliang Zou.
  Journal of Machine Learning Research (JMLR), Accepted, 2025.

• Selective Conformal Inference with False Coverage-statement Rate Control
  Yajie Bao, Yuyang Huo, Haojie Ren and Changliang Zou.
  Biometrika, 2024.

• Semi-Profiled Distributed Estimation for High-Dimensional Partially Linear Model
  Yajie Bao and Haojie Ren.
  Computational Statistics & Data Analysis, 2023.

• Varying Coefficient Linear Discriminant Analysis for Dynamic Data
  Yajie Bao and Yuyang Liu.
  Electronic Journal of Statistics, 2022.

Conference publication

• Conformal Robustness Control: A New Strategy for Robust Decision
  Yang Hu, Jieren Tan, Changliang Zou, Yajie Bao* and Haojie Ren*.
  International Conference on Learning Representations (ICLR), 2026.

• Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach
  Qian Peng, Yajie Bao*, Haojie Ren*, Zhaojun Wang, Changliang Zou.
  International Conference on Machine Learning (ICML), 2025.

• Error-quantified Conformal Inference for Time Series
  Junxi Wu, Dongjian Hu, Yajie Bao*, Shu-Tao Xia*, Changliang Zou.
  International Conference on Learning Representations (ICLR), 2025.

• Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective
  Yajie Bao, Michael Crawshaw and Mingrui Liu.
  International Conference on Machine Learning (ICML), 2024.

• Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization
  Yajie Bao, Amarda Shehu, Mingrui Liu.
  Advances in Neural Information Processing Systems (NeurIPS), 2023.

• Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
  Michael Crawshaw, Yajie Bao and Mingrui Liu.
  Advances in Neural Information Processing Systems (NeurIPS), 2023.

• EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
  Michael Crawshaw, Yajie Bao and Mingrui Liu.
  International Conference on Learning Representations (ICLR), 2023.

• Fast Composite Optimization and Statistical Recovery in Federated Learning
  Yajie Bao, Michael Crawshaw, Shan Luo and Mingrui Liu.
  International Conference on Machine Learning (ICML), 2022.

• Byzantine-Tolerant Distributed Multiclass Sparse Linear Discriminant Analysis
  Yajie Bao, Weidong Liu, Xiaojun Mao and Weijia Xiong.
  Conference on Uncertainty in Artificial Intelligence (UAI), 2022.

• One-Round Communication Efficient Distributed M-Estimation
  Yajie Bao and Weijia Xiong.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

Writtings

• Step into High-dimensional Statistics: Sparse Mean Estimation
  

• Linear Regression and M-estimator for Diverging p